Modal

AI infrastructure that developers love 💚

Run inference, sandboxes, batch processing, training, and many other things on Modal

[Modal @modal]

https://t.co/5DZKxwdGY5

[sona dolasia @teenychairs]

we're hosting some parties to celebrate our C 💚

exclusive swag at both ofc

[Erik Bernhardsson @bernhardsson]

I love to see @modal being used for biology and cutting-edge research like this. Very cool work from the team at @biohub to push open models forward in protein design and comp bio.

Here's how to run it on Modal: https://t.co/joiLmRw4pa

[Modal @modal]

We're bringing together our friends and community to celebrate our Series C.

Join us at Noguchi's Sunken Garden in NYC on June 16th or at the Legion of Honor in SF on June 25th.

Invites are limited, apply here: https://t.co/B4h0C7Wq0Q

[Modal @modal]

@_djdumpling Welcome!!

[Modal @modal]

@joshavata @MeckaAI 🚀

[Modal @modal]

Read the post: https://t.co/SInqTsiI5s

[Modal @modal]

Reinforcement learning has exploded on Modal, and we've been cooking.

Here's a review of lessons learned helping teams train at scale, the patterns we kept seeing, and an open-source library to get started with RL on Modal quickly.

[Modal @modal]

@botirkhaltaevv Welcome!

[Nan Jiang @nanjiangwill]

At @modal, we're working to make sure OSS RL frameworks have all the techniques necessary to train frontier open-weights models.

Delta compression is key, but the job's not done. There are still lots of open problems around weight sync, auto-scaling, & cross-cluster training.

My DMs are open!

[Modal @modal]

Cyber attackers don't wait for you to spin up infrastructure.

How @DoppelHQ uses Modal's elastic compute to scale inference, cut training overhead, and parallelize experimentation 👇

[Doppel @DoppelHQ]

How does Doppel stay ahead of phishing campaigns that spin up millions of domains at once?

Machine learning engineers William Gill and Ishana Shastri explain how they use @modal to:

- React to sudden cyberattack influxes with highly elastic GPU clusters.
- Replace complex Cloud Run and Docker setups with simple decorators in a single code file.
- Make experimentation cost-efficient by running parallel experiments in the background.

Learn more:
https://t.co/L6YTIFgAYs

[Modal @modal]

@DoppelHQ 🤝

[Modal @modal]

Deploy today: https://t.co/OgUnC3GPRo

[Modal @modal]

Day 0 support for Step 3.7 Flash on Modal.

- 198B parameter MoE with 11B active
- 256K context
- 3 reasoning levels
- Native image & video understanding

Great to work with @StepFun_ai and @sgl_project on this one.

[Modal @modal]

Learn more: https://t.co/XGczFQvOAD

[Modal @modal]

Agents do a lot more than just write code now, and infrastructure guardrails need to keep up.

Role-Based Access Control is now available to all Workspaces on Team and Enterprise plans.

[Steven Feng @stevenyfeng]

Stanford CS25 Talk TOMORROW (Thurs, 5/28) at 4:30pm PST 🤖

Charles Frye (@charles_irl) from @modal on: Serving Transformers: Lessons from the trenches of production inference

Training models is only half the battle. Actually serving them at scale is where things get real 👇 (1/6)

[Modal @modal]

Today @biohub released the next generation of open models in the ESM family.

These models achieve SOTA results in understanding how proteins interact with other molecules, essential for designing new drugs and antibodies.

Try ESMFold2 on Modal: https://t.co/sbw830TNHw

[Alex Rives @alexrives]

Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology.

The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics.

We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity.

We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures.

ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences.

A world model of protein biology emerges through language modeling.

We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins.

The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science.

This understanding emerges without prior knowledge, just from language modeling of protein sequences.

Language models are becoming a powerful substrate to understand and program biology.

The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders.

I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.

[Modal @modal]

Modal has been named to @Redpoint's 2026 InfraRed 100 — a list of 100 standout private companies shaping the future of infrastructure.

Check out the full list here: https://t.co/18BE16RJMW

[The Information @theinformation]

Today on TITV:

-OpenAI’s next ad move: Going small to scale big | @anngehan

-@modal founder & CEO @bernhardsson on $4.5B valuation hopes and AI playbook

-Twilio’s AI boost is a double-edged sword | @AnitaRamaswamy

[Modal @modal]

@brooke_jamieson 👀